Measurements of Losses and Reynolds Stresses in the Secondary Flow Downstream of a Low-Speed Linear Turbine Cascade
Bibliographic record
Abstract
Experimental measurements of the mean and turbulent flow field were preformed downstream of a low-speed linear turbine cascade. The influence of turbulence on the production of secondary losses is examined. Steady pressure measurements were collected using a seven-hole pressure probe and the turbulent flow quantities were measured using a rotatable x-type hotwire probe. Each probe was traversed downstream of the cascade along planes positioned at three axial locations: 100%, 120%, and 140% of the axial chord (Cx) downstream of the leading edge. The seven-hole pressure probe was used to determine the local total and static pressure as well as the three mean velocity components. The rotatable x-type hotwire probe, in addition to the mean velocity components, provided the local Reynolds stresses and the turbulent kinetic energy. The axial development of the secondary losses is examined in relation to the rate at which mean kinetic energy is transferred to turbulent kinetic energy. In general, losses are generated as a result of the mean flow dissipating kinetic energy through the action of viscosity. The production of turbulence can be considered a preliminary step in this process. The measured total pressure contours from the three axial locations (1.00, 1.20, and 1.40Cx) demonstrate the development of the secondary losses. The peak loss core in each plane consists mainly of low momentum fluid that originates from the inlet endwall boundary layer. There are, however, additional losses generated as the flow mixes with downstream distance. These losses have been found to relate to the turbulent Reynolds stresses. An examination of the turbulent deformation work term demonstrates a mechanism of loss generation in the secondary flow region. The importance of the Reynolds shear stresses to this process is explored in detail.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".